Merge pull request #456 from EIT-ALIVE/joss/update-features

Update joss-paper

Author: psomhorst

paper/paper.md

@@ -103,7 +103,7 @@ international community has been invited to use and contribute to the software.
 ## Key features
 
 `eitprocessing` aims to simplify and standardize loading, pre-processing, analysis and reporting
-or respiration-related datasets.
+of respiration-related datasets.
 Notebooks demonstrating these features are available in the repository.
 
 ### Loading
@@ -134,11 +134,14 @@ for the first part of a measurement and prone for the second part.
 
 `eitprocessing` currently has implementations for the following pre-processing steps:
 
+- high-pass, low-pass, band-pass or band-stop Butterworth filters, as well as a Multiple Digital Notch filter @Wisse2024-wi;
 - calculation of the global or regional impedance as the sum of the impedance of all or a subset of pixels;
-- high-pass, low-pass, band-pass or band-stop Butterworth filters;
 - a moving averager using convolution with a given window;
+- region of interest selection using predefined or custom masks;
+- functional lung space detection using the tidal impedance variation, amplitude, or the Watershed method;
 - automatic detection of the start, middle (end-inspiration) and end of breaths on a
-  global/regional and pixel level.
+  global/regional and pixel level;
+- automatic detection of the respiratory and heart rate from pixel impedance values.
 
 ### Analysis
 
@@ -147,16 +150,25 @@ for the first part of a measurement and prone for the second part.
 - end-expiratory lung impedance on a global/regional and pixel level;
 - tidal impedance variation on a global/regional and pixel level.
 
+## Visualization
+
+`eitprocessing` includes several visualization methods to simplify and standardize visual output. Examples are:
+
+- showing pixel maps, e.g., tidal impedance variation, changes in EELI, pendelluft, etc.;
+- show the effect of filtering methods in the frequency domain.
+
 ## Future perspective
 
 `eitprocessing` is ready for use in offline analysis of EIT and respiratory related data. Our team
 is actively working on expanding the features of the software.
 
 Several features are in active development. Examples are:
 
-- more advanced filtering methods, using a combination of Butterworth filters, empirical mode
-  decomposition or wavelet transforms;
-- automatic detection of respiratory and heart rate from pixel impedance values.
+- provenance tracking of data processing steps;
+- more advanced filtering methods, e.g., empirical mode
+  decomposition and wavelet transforms;
+- quantification of pendelluft;
+- expansion of visualization methods.
 
 Moreover, we plan to extend `eitprocessing` with standardized workflows to summarize and report
 analysis results.